{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b6162ff-28c5-4566-ae44-c8fdffd214dc",
   "metadata": {},
   "source": [
    "# 芯片检测练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdf6664d-8193-4491-98a4-8a6de371fe0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test1</th>\n",
       "      <th>test2</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      test1    test2  Pass\n",
       "0  0.051267  0.69956     1\n",
       "1 -0.092742  0.68494     1\n",
       "2 -0.213710  0.69225     1\n",
       "3 -0.375000  0.50219     1\n",
       "4 -0.513250  0.46564     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 读取数据\n",
    "data = pd.read_csv('chip_test.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1a98ba7-33fb-49f1-a284-6125111efd82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据\n",
    "# 启用嵌入显示功能\n",
    "\n",
    "%matplotlib inline \n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig1 = plt.figure()\n",
    "e1 = data.loc[:,'test1']\n",
    "e2 = data.loc[:,'test2']\n",
    "\n",
    "plt.scatter(e1, e2, c = 'b')\n",
    "plt.title('test1 - test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce5d8a66-3f05-43fa-ab2b-7d969a23c725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       True\n",
      "1       True\n",
      "2       True\n",
      "3       True\n",
      "4       True\n",
      "       ...  \n",
      "113    False\n",
      "114    False\n",
      "115    False\n",
      "116    False\n",
      "117    False\n",
      "Name: Pass, Length: 118, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# add lable mask\n",
    "mask = data.loc[:,'Pass'] == 1\n",
    "print(mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d72f6205-12fb-4b32-88b5-44370d4c786f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(facecolor='w')\n",
    "\n",
    "plt.gca().set_facecolor('black') \n",
    "# 根据上面的标签mask来区分不同的点，如果不给出颜色，也是自动换成连个颜色的\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask], data.loc[:,'test2'][mask], c = 'green')\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask], data.loc[:,'test2'][~mask], c = 'red')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "\n",
    "plt.title('test1(X-Label) - test2(Y-Label)')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01000053-f27c-4c08-9274-df1dc2fcc3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据，用于训练\n",
    "X = data.drop(['Pass'],axis = 1) # 从data中剥离结果\n",
    "y = data.loc[:,'Pass']\n",
    "\n",
    "X1 = data.loc[:,'test1']\n",
    "X2 = data.loc[:,'test2']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb8c71c0-87f8-4824-b9ac-82f0fad304ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118, 2) (118,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91ffc24a-88a0-417f-8bff-cfdb30aa9a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           X1        X2      X1_2      X2_2     X1_X2\n",
      "0    0.051267  0.699560  0.002628  0.489384  0.035864\n",
      "1   -0.092742  0.684940  0.008601  0.469143 -0.063523\n",
      "2   -0.213710  0.692250  0.045672  0.479210 -0.147941\n",
      "3   -0.375000  0.502190  0.140625  0.252195 -0.188321\n",
      "4   -0.513250  0.465640  0.263426  0.216821 -0.238990\n",
      "..        ...       ...       ...       ...       ...\n",
      "113 -0.720620  0.538740  0.519293  0.290241 -0.388227\n",
      "114 -0.593890  0.494880  0.352705  0.244906 -0.293904\n",
      "115 -0.484450  0.999270  0.234692  0.998541 -0.484096\n",
      "116 -0.006336  0.999270  0.000040  0.998541 -0.006332\n",
      "117  0.632650 -0.030612  0.400246  0.000937 -0.019367\n",
      "\n",
      "[118 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# 二级边界函数\n",
    "# 创建新的数据\n",
    "\n",
    "X1_2 = X1 * X1\n",
    "X2_2 = X2 * X2\n",
    "X1_X2 = X1 * X2\n",
    "X_new = {'X1':X1,'X2':X2,'X1_2':X1_2,'X2_2':X2_2,'X1_X2':X1_X2}\n",
    "X_new = pd.DataFrame(X_new)\n",
    "print(X_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e52ebc2-449e-4fe3-8c89-803dac19c649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建新的模型\n",
    "LR2 = LogisticRegression()\n",
    "LR2.fit(X_new, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f826b79-0058-4bf5-b611-928c921852ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0\n",
      " 0 0 0 1 0 0 1]\n",
      "0.8135593220338984\n"
     ]
    }
   ],
   "source": [
    "y2_predict = LR2.predict(X_new)\n",
    "print(y2_predict)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy2 = accuracy_score(y, y2_predict)\n",
    "print(accuracy2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f158ca12-6da3-4ffa-9084-e63b83ce4aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.35006804,  0.67151364, -2.78163007, -2.39042958, -0.95691685]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可视化\n",
    "LR2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1f297f8c-b3ca-43bd-9bd9-b7650dccdadd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x1):\n",
    "    x1_sort = np.sort(x1)\n",
    "    a = theta4\n",
    "    b = theta5 * x1_sort + theta2\n",
    "    c = theta0 + theta1 * x1_sort + theta3 * x1_sort * x1_sort\n",
    "    X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    return X2_new_boundary1,X2_new_boundary2\n",
    "\n",
    "\n",
    "def ff(x1):\n",
    "    # 确保输入是numpy数组并排序\n",
    "    x1 = np.array(x1)\n",
    "    x1_sort = np.sort(x1)\n",
    "    \n",
    "    # 计算二次方程系数\n",
    "    a = theta4\n",
    "    b = theta5 * x1_sort + theta2\n",
    "    c = theta0 + theta1 * x1_sort + theta3 * (x1_sort ** 2)\n",
    "    \n",
    "    # 计算判别式\n",
    "    discriminant = b**2 - 4*a*c\n",
    "    \n",
    "    # 创建结果数组，初始化为NaN\n",
    "    X2_new_boundary1 = np.full_like(x1_sort, np.nan)\n",
    "    X2_new_boundary2 = np.full_like(x1_sort, np.nan)\n",
    "    \n",
    "    # 只对判别式非负的位置计算平方根\n",
    "    valid_indices = discriminant >= 0\n",
    "    if np.any(valid_indices):\n",
    "        sqrt_discriminant = np.sqrt(discriminant[valid_indices])\n",
    "        X2_new_boundary1[valid_indices] = (-b[valid_indices] + sqrt_discriminant) / (2*a)\n",
    "        X2_new_boundary2[valid_indices] = (-b[valid_indices] - sqrt_discriminant) / (2*a)\n",
    "    \n",
    "    return X2_new_boundary1, X2_new_boundary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1d6a6ac7-d946-4ae3-b430-7348386d0bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.19949075] 0.35006804194101704 0.6715136404585055 -2.7816300658355058 -2.3904295834119336 -0.95691685357017\n"
     ]
    }
   ],
   "source": [
    "theta0 = LR2.intercept_\n",
    "theta1 = LR2.coef_[0][0]\n",
    "theta2 = LR2.coef_[0][1]\n",
    "theta3 = LR2.coef_[0][2]\n",
    "theta4 = LR2.coef_[0][3]\n",
    "theta5 = LR2.coef_[0][4]\n",
    "print(theta0,theta1,theta2,theta3,theta4,theta5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a85802da-8602-4827-9943-25aa8e879e40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.051267\n",
      "1     -0.092742\n",
      "2     -0.213710\n",
      "3     -0.375000\n",
      "4     -0.513250\n",
      "         ...   \n",
      "113   -0.720620\n",
      "114   -0.593890\n",
      "115   -0.484450\n",
      "116   -0.006336\n",
      "117    0.632650\n",
      "Name: test1, Length: 118, dtype: float64 112   -0.83007\n",
      "86    -0.75518\n",
      "84    -0.74366\n",
      "111   -0.72638\n",
      "113   -0.72062\n",
      "        ...   \n",
      "70     0.89804\n",
      "65     0.92684\n",
      "68     0.93836\n",
      "67     0.96141\n",
      "101    1.07090\n",
      "Name: test1, Length: 118, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18317/2600866615.py:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "/tmp/ipykernel_18317/2600866615.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X2_new_boundary1,X2_new_boundary2 = f(X1)\n",
    "fig7 = plt.figure()\n",
    "print(X1, X1_sort)\n",
    "plt.plot(X1_sort,X2_new_boundary1)\n",
    "plt.plot(X1_sort,X2_new_boundary2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8bc23a85-89d2-4c93-8a2b-19f406de69db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18317/2600866615.py:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "/tmp/ipykernel_18317/2600866615.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8 = plt.figure()\n",
    "\n",
    "X2_new_boundary1,X2_new_boundary2 = f(X1)\n",
    "\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask], data.loc[:,'test2'][mask],c='green')\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask], data.loc[:,'test2'][~mask],c='pink')\n",
    "plt.title('test1 - test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.plot(X1_sort,X2_new_boundary1,c='r')\n",
    "plt.plot(X1_sort,X2_new_boundary2,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "29ba9b9d-e86f-46ce-993d-a97052bb4284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAVNNJREFUeJzt3Xl8TOf+B/DPZLcko0Rii1hqiZ0gElVVxFpLFxShqsptXVvrlrqtpb3XrV8XpZbaW0Wp0lKq0tolqL2IWCtBIqJMYksiOb8/vh0RCTIxZ56Zyef9ep3XOZmckW+OJPOZ5zyLQdM0DUREREQOwkV1AURERESWYHghIiIih8LwQkRERA6F4YWIiIgcCsMLERERORSGFyIiInIoDC9ERETkUBheiIiIyKG4qS7A2rKysnDx4kV4e3vDYDCoLoeIiIjyQdM0pKamoly5cnBxeXjbitOFl4sXLyIgIEB1GURERFQA8fHxqFChwkPPcbrw4u3tDUC+eR8fH8XVEBERUX6kpKQgICDg7uv4wzhdeDHfKvLx8WF4ISIicjD56fLBDrtERETkUBheiIiIyKEwvBAREZFDYXghIiIih8LwQkRERA6F4YWIiIgcCsMLERERORSGFyIiInIoDC9ERETkUBheiIiIyKEwvBAREZFDYXghIiIih+J0CzMSFUq3bwMmU87t5k15PC1N9vduaWlAZiagadkbkPPY1RXw9AQ8PLL39x4XLQp4e8vm45N97O0tzyUi0gnDC5G90TTg2jXgwgUgKQm4dEn25s388ZUr2UElPV111TkVLQoYjYCvr2ylS2cfmz8uXRooVw4oX17CDxFRPjG8ENmapgEXLwInTwLnzgHx8UBcnGzm4+vXC/Zv+/hIaDAagWLFAC8v2Tw9s4/NH7v9/etvMGRv5o8BaZlJT5dWmvv3aWnSspOamr2lpAAZGfLcmzdlS0jIX93Fi0uIuX+rVAmoWlX2RYoU7JoQkdNheCHSy9WrwLFjwIkTElROnpTjU6fkhf1RSpUC/P0BP7/svXnz95fPm4OK0Si3a1wUd2NLS8sOM1evSutQcjJw+bLszdvly9J6dOGCtBxdvw7Exsr2IOXKAVWq5NyefBIICgJKlLDZt0hE6hk0zXyD2zmkpKTAaDTCZDLBh03RZAu3bwMxMcAff+TcLl588HNcXYHKlWULCAAqVpTNfFyhgtx6KQxu3JAQc/92/jxw9ixw+rSEoYcpU0ZCzP1b2bLZLUlEZNcsef1meCGyRFoacOgQ8Pvvsu3dCxw/LrdY8lKxIlC9umzVqmVvlSsD7u62rd1RaRrw11/AmTM5t9OnpSXrwoUHP9doBOrXBxo0kK1+faB2bbltRkR2heGF4YWsQdPkBXLHDmD3bgkrhw9n9+u41xNPAHXr5tzq1GFHVFtISZEAGRMjm/n49Om8Q6Wbm7TKmANN48ZAcLD0ESIiZRheGF6oIDIz5XbP9u3ZW2Ji7vNKlQKaNMneGjWS/hi8PWFf0tKkD82hQ8DBg9nbX3/lPtfVVcJmSAjQtKnsg4I45JvIhhheGF4oPzRN3qFv3Aj8+qu0sJhMOc/x8JAXs9DQ7LASGMig4qg0TfrSmIPMgQPSqpZX/yRvb2mVCQsDnn5afga8vW1dMVGhwfDC8EIPkpQkQSUyUkLL/S9a3t7yYtWihWxNm8rQYnJuFy5IiDFve/dKR+J7ubpKK1uLFhJmnnpKWuGIyCoYXhheyEzT5B32mjWy7d+f8/NeXvJiFB4OtGolHTrdOINAoZeZKcPcd+2SFrlt24A//8x9Xp06QMuWQJs28vNjNNq8VCJnwfDC8FK4pacDW7ZkB5b4+Jyfb9AAaNtWAkvz5pz8jPInLi67L9S2bXLL8V6urtJS17atbCEhHFFGZAGGF4aXwictDfjlF2D5cmDt2pzzghQtKkGla1egQweZ4I3ocSUlSZDZtEluQ548mfPz3t7AM8/Iz17HjjKpHhE9kCWv3zaZjnPmzJmoXLkyvLy8EBwcjO3btz/0/CVLlqB+/fooWrQoypYtiwEDBuDKlSu2KJUcSUYGsGED8MorEki6dgWWLpXgUqYM8PrrwE8/yYyuq1dnn0dkDX5+wAsvADNmyHwzf/4JzJ0L9OghfWFSUyVI//OfssRB7drAO+9I4LlzR3X1RA5N95aX5cuXIyIiAjNnzkTz5s3x5ZdfYt68eTh27BgqVqyY6/wdO3agZcuW+Oyzz/Dcc8/hwoULGDJkCKpVq4bVq1c/8uux5cXJaRoQHQ189RXw/fcy/bxZ+fLywtGjhzTfq54qnwqvrCwZyRQZCfz8M7BzZ845Z0qWlFbAzp2Bdu1kniCiQs6ubhuFhISgUaNGmDVr1t3HgoKC0K1bN0yePDnX+R9//DFmzZqF06dP331s+vTpmDJlCuLv77uQB4YXJ3XhAvD118CiRfIu18zPD3jpJaBnT+m/wsBC9ujqVbmtuXathJmrV7M/5+oqnX1ffBHo1o2tg1Ro2c1to/T0dOzbtw/h4eE5Hg8PD0dUVFSezwkLC8P58+exfv16aJqGS5cuYeXKlejUqVOe56elpSElJSXHRk4iLU36sLRvL9Psv/uuBJeiRYH+/WXI84ULwBdfyIghBheyV088AfTqBSxZIn1ltm0D/vUvmQgvM1N+locMkckOn3lGfqYftjYWUSGn61/75ORkZGZmwv++dxL+/v5IzGvmUkh4WbJkCXr27AkPDw+UKVMGJUqUwPTp0/M8f/LkyTAajXe3gIAAq38fZGNxcRJUAgLkD/4vv0gzfIsWwIIFMuvtokVA69Yc1kyOx81NfpY/+ih71fH//U8mxMvKArZulX4y5ctLa+Jnn8nEekR0l03eqhrum41U07Rcj5kdO3YMw4YNw/vvv499+/Zhw4YNOHv2LIYMGZLn+WPHjoXJZLq75efWEtmhrCyZNK5bN1m0cPJk4PJl+QM+bpyM5Ni2DRgwgLOcknOpVk068v7+u6yi/cknMpsvAERFAaNGSctjq1bAvHnAtWtKyyWyB7r2eUlPT0fRokXx3XffoXv37ncfHz58OA4ePIitW7fmek5ERARu376N77777u5jO3bsQIsWLXDx4kWULVv2oV+TfV4czI0bwMKFwLRpOYeatm4NvPEG0KULW1eocDp/XkbJrVghE+WZeXgAnToBffrInjNAk5Owmz4vHh4eCA4ORmRkZI7HIyMjERYWludzbt68CZf7+i64/r04mpNNSVO4XboEvPeevKP85z8luPj4AMOGyeRfv/4KPP88gwsVXhUqyO/G9u0yDHvyZJnRNz1dQs2LL8qUAAMHSqsk/z5SIWKzodKzZ89GaGgo5syZg7lz5+Lo0aMIDAzE2LFjceHCBXz99dcAgEWLFmHQoEGYNm0a2rVrh4SEBIwYMQIuLi7YvXv3I78eW17sXGws8OmnMtQ5LU0eq1pVmsb79QOKF1dbH5G9O3xYOv4uXZqzL0y1asCrr8rvUbly6uojKiCLXr81G5gxY4YWGBioeXh4aI0aNdK2bt1693P9+/fXWrZsmeP8adOmabVq1dKKFCmilS1bVuvTp492/vz5fH0tk8mkAdBMJpM1vwV6XIcPa9qLL2qawaBp8h5R00JCNG3lSk27c0d1dUSOJzNT07Zs0bRXX9W04sWzf69cXTWtc2dNW71a09LTVVdJlG+WvH5zeQDS1+HDwKRJMqGcWZcuwOjRMpLiAR23icgC168D330HzJ8vE+KZ+flJS8zrr0vLDJEds6tJ6myN4cVOHDokoWXVKvnYYJDJ5N57T+7bE5E+YmNlSoGvvpK+ZWbh4dIJvnNnmRiPyM4wvDC8qHP2rAxtXrZMPjYYZLr+996TtV2IyDYyMmQ23zlzgPXrszv0VqwIDB4MvPaatMwQ2Qm7GW1EhUhyMjBiBFCjRnZw6dkTOHIE+PZbBhciW3N3l1u0P/0EnD4tM/qWKiWTQI4bJ6OZ+vSRuWSc6z0sFQIML/R4bt4E/vtfGTH0+efybq9tW2D/fgkttWqprpCIKleWGX3Pn5fbSU2byu/q0qXS9ywkRJbi4GrX5CAYXqhgNE06CNasKe/iUlKAhg1lltyNG+WYiOyLl5d04N29W2b0HTAA8PSU41695E3Ip5/K7zORHWN4IcsdPSoz4PboAcTHA4GBwDffAHv3SqsLEdm/xo2lY29cHDB+PODrK8dvvSXrir39tnxMZIcYXij/rl2Tfi316wObN8u7uAkTZEbcPn24qjORI/Lzk9/juDjp3FuzprS8fPIJUKUK0Ls38McfqqskyoGvNvRomib9V2rUkH4tmZkydX9MjLxjK1JEdYVE9LiKFAEGDZKW1XXrpHU1M1M64NerB3TtCuzZo7pKIgAML/QocXHAc88BL78MJCXJu7LISJl0rlIl1dURkbW5uAAdO8r6Yvv3y/xMBgOwZo107G3bVlpeOUKJFGJ4obxlZgLTp8sQ53XrZCXbSZNk8rk2bVRXR0S20LChrGodEwO88ooslPrrr8Czz8oopXXrGGJICYYXyi02FmjRQlZ4vn5d/kgdPCgTzXl4qK6OiGytRg1g4ULg1CmZpdfTE4iOltl6mzYFNmxgiCGbYnihbJoGzJgh77aiowFvb2DmTGDbNiAoSHV1RKRaYKD8jTh7VtYnK1ZMRhl26AA89ZTcTiKyAYYXEhcuAO3bA0OHArduya2ho0eBf/yDo4iIKKeyZYEpU4AzZ2RotZeXzNT77LOy3bs4JJEO+KpEck+7bl2ZXM7LC5g2DfjlF5nrgYjoQfz8gI8/luUHhg6V28qbN0srTPv20ipDpAOGl8Ls1i3g9ddlDaKrV4HgYODAAeCf/2RrCxHlX7ly0sH/5EkZbu3mJm+AmjSReWLOnlVdITkZvkIVVrGxMuxx7lwZBvnvf0s/l5o1VVdGRI6qYkWZ6C42FoiIkL8ty5ZJh99Ro4ArV1RXSE6C4aUwWrJEWln++EOafTduBD74QFahJSJ6XFWqAF9/LfPEtG0ri0B+9pmsnTRlirT6Ej0GhpfCJD0dGDIE6NsXuHEDaNVKhkBz3hYi0kODBvLm6JdfZFkRkwl45x1piVm8GMjKUl0hOSiGl8Li0iUZBfDll9KUO368zJRbtqzqyojI2YWHA/v2AV99JQMB4uNldeuwMFnRmshCDC+Fwd69soLszp2A0SizYk6YALi6qq6MiAoLV1cJLLGxwOTJQPHiwO7dMsndq68CiYmqKyQHwvDi7JYskdlyz5+Xzrh79siEUkREKhQpAowZA5w4IWEGkNl7q1eXYdfp6WrrI4fA8OKsNA14/33p33L7NtCpE7Brl/yBICJSrWxZuY0UHS1DqlNTZdbeunWBn39WXR3ZOYYXZ5SeDgwYICOIAHmX8+OPcsuIiMieNGsmb6wWLAD8/aVFpmNH4MUXZeZvojwwvDiblBRpZfnqK7nHPGeO3F9m/xYislcuLvKG68QJWW7A1RX4/ntZU23aNFnlnugeDC/O5MIF6d/y66+yYNratTLbJRGRI/DxkX4v+/ZJi0xqKjB8uEyouW+f6urIjjC8OIszZ2Q9kcOHgTJlZCVodswlIkdUv76Mjpw1S25379sno5KGD5fWZSr0GF6cwbFjElz+/BN48knpANeokeqqiIgKzsVFJtU8fhx4+WWZ0G7aNKBWLWD9etXVkWIML45u/36gZUsgIQGoU0daXCpVUl0VEZF1lCkDLF0qs/RWrSq3xzt1Avr3lwVlqVBieHFk0dEyxX9yskxCt2ULZ8wlIucUHi63xUeOlFnCv/5aWmF+/FF1ZaQAw4uj+v13oH17uf/bogXw229AqVKqqyIi0k/RosCnnwI7dsj6SImJQLduQO/e8iaOCg2GF0d08KC8C0lJAZ5+WiZ08vFRXRURkW2EhQEHDgD/+pf0jVm2DKhdG1i9WnVlZCMML47myBFZBfraNSA0FPjpJxkWTURUmBQpAnz0kUxwV7s2kJQEPP88MHCgDLEmp8bw4khOnABatwauXJHptH/+GfD2Vl0VEZE6TZrIUOp33pG+MAsWAA0aAFFRqisjHTG8OIrERKBdO3l30aCB9LzndP9ERICnJ/C//8mghYoVZd6rFi1kfbeMDNXVkQ4YXhxBSopMOPfnnzJU8JdfgCeeUF0VEZF9efppGZEUESHzwnzwAdC8ubRak1OxSXiZOXMmKleuDC8vLwQHB2P79u0PPT8tLQ3jxo1DYGAgPD09UbVqVSxYsMAWpdqf9HS5j3vwIODnJ8HFz091VURE9slolGHU334LlCghIzMbNgQWLVJdGVmR7uFl+fLlGDFiBMaNG4cDBw6gRYsW6NChA+Li4h74nB49euC3337D/PnzERsbi2XLlqFmzZp6l2p/NE0WK/vtN6B4cZlVsmpV1VUREdm/nj2BP/4Ann0WuHlT/pb27w9cv666MrICg6Zpmp5fICQkBI0aNcKsWbPuPhYUFIRu3bph8uTJuc7fsGEDevXqhTNnzqBkyZIWf72UlBQYjUaYTCb4OPrw4UmTgPHjATc3YN06GR5NRET5l5kp/WHef19uJdWsCaxYAdStq7oyuo8lr9+6trykp6dj3759CL/vRTc8PBxRD+gJvmbNGjRu3BhTpkxB+fLlUb16dbz99tu4detWnuenpaUhJSUlx+YUvv9eggsAzJ7N4EJEVBCursC4ccDmzUC5crJWUtOmwLx50rpNDknX8JKcnIzMzEz4+/vneNzf3x+JiYl5PufMmTPYsWMHjhw5gtWrV2Pq1KlYuXIl3nzzzTzPnzx5MoxG490tICDA6t+HzR08CPTrJ8fDh8u8BUREVHBPPy1/W9u3B27fBgYNAvr04ZwwDsomHXYNBkOOjzVNy/WYWVZWFgwGA5YsWYKmTZuiY8eO+PTTT7Fo0aI8W1/Gjh0Lk8l0d4uPj9fle7CZpCSga1e5R9u2LfDxx6orIiJyDqVLyy34jz6SFplly4BmzTgayQHpGl58fX3h6uqaq5UlKSkpV2uMWdmyZVG+fHkY75nDJCgoCJqm4fz587nO9/T0hI+PT47NYWVmytLvcXFA9erA8uXS34WIiKzDxUWWFdi2TW4jHTsmE92tWaO6MrKAruHFw8MDwcHBiIyMzPF4ZGQkwsLC8nxO8+bNcfHiRVy/p0f4iRMn4OLiggoVKuhZrnoffABs2iSLj61ezblciIj0EhYmM/O2aCFzaXXtKp16MzNVV0b5oPtto1GjRmHevHlYsGABYmJiMHLkSMTFxWHIkCEA5LZPP3P/DgC9e/dGqVKlMGDAABw7dgzbtm3D6NGj8eqrr6JIkSJ6l6tOZKSMLgKAL7+Upd6JiEg/ZcrIVBTDhsnHH3wAPPcccPWq2rrokXS/J9GzZ09cuXIFkyZNQkJCAurUqYP169cjMDAQAJCQkJBjzpfixYsjMjIS//znP9G4cWOUKlUKPXr0wIcffqh3qepcvCgdxzRNOpH17au6IiKiwsHdHfj8c7l19PrrsmZc48bAjz8Cdeqoro4eQPd5XmzN4eZ5ycqSjrmbNgH16wPR0bJaKhER2dbBg0D37rIUi7e3zNLbsaPqqgoNu5nnhfJh2rTsfi4rVjC4EBGp0qABsHcv0KqVDKF+7jngs884H4wdYnhR6ehRYMwYOf7kExlhRERE6pQqBWzYALz2mrSMjxoFDB4s68yR3WB4USU9XVY+TUuTFaMHD1ZdERERAYCHBzBnDvDpp4DBAMydC7RrB/z1l+rK6G8ML6p8+CFw4ICk/Pnz5ReEiIjsg8EAjBwJrF0rC+Nu2QKEhACnTqmujMDwosaRI4B5UcpZs4CyZdXWQ0REeevUSQZSBAZKcAkLA/bsUV1VocfwYmtZWXKL6M4dmRTpxRdVV0RERA9Tpw6wezfQqBFw+bJ06F23TnVVhRrDi63NmQNERUkz5PTpvF1EROQI/P3l1lG7drL2XNeucsuflGB4saWLF4F33pHj//4XcIYVsImICgtvb+kD06+fLCPw2msyMzqHUtscw4stjRkja2g0bQq88YbqaoiIyFLu7sCiRcC778rH48cD//gH10SyMYYXW9m9G1i8WI6/+EKWYyciIsdjMAD/+Q8wc6asUv3llzL1RUaG6soKDYYXW9A0YMQIOe7fX9bQICIix/aPfwBLlwJubsCyZcALLwC3b6uuqlBgeLGFZcuAXbuAYsWkrwsRETmHnj2BH34AvLykP0znzsD166qrcnoML3q7fTt7CYCxY4Fy5dTWQ0RE1tWpk6xGXbw48NtvQHg4cO2a6qqcGsOL3r78EoiPBypUkDUyiIjI+TzzDPDrr8ATT8ikds88AyQnq67KaTG86OnGjezbRO+9xxWjiYicWUiIzAXj7w8cOgS0bg1cuaK6KqfE8KKn6dOBpCSgShVgwADV1RARkd7q1csOMIcPM8DohOFFLyYTMGWKHE+YIHMDEBGR86tZE9i8ObsFpk0brkhtZQwvepk1C7h6FQgKAnr3Vl0NERHZUlAQsGkT4OcHHDzIAGNlDC96uH0b+PxzOR47lhPSEREVRrVqSYApXRo4cABo25ajkKyE4UUPixcDiYmydlGvXqqrISIiVWrXlltIpUsD+/fLsOobN1RX5fAYXqwtMxP4+GM5HjmSfV2IiAq72rVlGHWJEkBUlMzEm56uuiqHxvBibWvXAidOyFj/QYNUV0NERPagXj1g/XqgaFHgl1+APn24mONjYHixti++kP3gwTLbIhEREQCEhspSAh4ewMqV8jqhaaqrckgML9Z04oRMDW0wAEOGqK6GiIjsTdu2st6diwswfz4wejQDTAEwvFjT7Nmy79QJCAxUWwsREdmn558H5s2T408+AT79VG09DojhxVpu3gQWLpTjf/xDbS1ERGTfBgwA/u//5Pjtt4HvvlNbj4NheLGW77+X8fuVKgHt2qmuhoiI7N1bbwFDh8pxRASwY4faehwIw4u1LF4s+wEDOCkdERE9msEATJ0KdO0KpKUBXboAsbGqq3IIDC/WcPGidNQFgL591dZCRESOw9UVWLpUVqS+ehXo0AG4dEl1VXaP4cUavv0WyMoCwsJkBWkiIqL8KlpU5girWhU4e1ZaYG7dUl2VXWN4sYZvvpE9W12IiKggSpcGfv4ZKFkS2LMHeP11DqF+CIaXx3XihCy45eYG9OihuhoiInJU1arJqCNXV3lTbB6NRLkwvDyuH3+UfatWQKlSamshIiLH9uyzwOefy/GYMcC6dWrrsVMML4/rhx9k362byiqIiMhZvPFG9tIBL78MHDumuiK7Y5PwMnPmTFSuXBleXl4IDg7G9u3b8/W8nTt3ws3NDQ0aNNC3wIK6dAmIjpbjLl3U1kJERM7BYACmTQOefhpITZXXl6tXVVdlV3QPL8uXL8eIESMwbtw4HDhwAC1atECHDh0QFxf30OeZTCb069cPrVu31rvEglu7VpJx48ZAhQqqqyEiImdhXryxUiXg9GmgXz8Z1UoAbBBePv30UwwcOBCvvfYagoKCMHXqVAQEBGDWrFkPfd7gwYPRu3dvhIaG6l1iwf38s+yfe05tHURE5HxKl5bZ2z09gZ9+AqZMUV2R3dA1vKSnp2Pfvn0IDw/P8Xh4eDiioqIe+LyFCxfi9OnTGD9+/CO/RlpaGlJSUnJsNpGZCWzeLMf3fX9ERERW0agRMH26HI8bB2zZorQce6FreElOTkZmZib8/f1zPO7v74/ExMQ8n3Py5EmMGTMGS5YsgZub2yO/xuTJk2E0Gu9uAQEBVqn9kQ4elHuQPj5y24iIiEgPr70G9O8vt4169QISElRXpJxNOuwaDIYcH2ualusxAMjMzETv3r0xceJEVK9ePV//9tixY2Eyme5u8fHxVqn5kX79VfbPPCNzvBAREenBYABmzgTq1pWBIj17AhkZqqtSStfw4uvrC1dX11ytLElJSblaYwAgNTUVe/fuxdChQ+Hm5gY3NzdMmjQJhw4dgpubGzZt2pTrOZ6envDx8cmx2YR5LSN77lBMRETOoWhR6cDr7Q1s3w5MnKi6IqV0DS8eHh4IDg5GZGRkjscjIyMRFhaW63wfHx/88ccfOHjw4N1tyJAhqFGjBg4ePIiQkBA9y82/zEzA3GenVSu1tRARUeFQvTowb54c//e/wNatautRSPf7HaNGjUJERAQaN26M0NBQzJkzB3FxcRgyZAgAue1z4cIFfP3113BxcUGdOnVyPN/Pzw9eXl65Hlfq6FHgxg2geHGgVi3V1RARUWHRowewYQOwcKGsp3f4MPDEE6qrsjndw0vPnj1x5coVTJo0CQkJCahTpw7Wr1+PwMBAAEBCQsIj53yxO7t3y75pU1mDgoiIyFamTZNbR6dOyQKOK1ZIv5hCxKBpzrVsZUpKCoxGI0wmk379X157DZg/Hxg7VpruiIiIbOn334GwMODOHXk9evVV1RU9Nktev7m2UUHs2iV7e+mDQ0REhUuTJsAHH8jxsGHSClOIMLxY6vZt4PhxOQ4OVlsLEREVXqNHy3QdN24AAwcWquUDGF4sFRsro41KlADKl1ddDRERFVaursCCBUCxYsC2bcAXX6iuyGYYXix19Kjs69QpdB2kiIjIzlSuDPzf/8nxmDGF5vYRw4uljhyRvT0N3SYiosJr8GCZc+zWrUJz+4jhxVLm8FK7tto6iIiIAMDFRUYcFaLbRwwvljp5UvY1a6qtg4iIyOze20fvvgs42vxpFmJ4sYSmAX/+KceVKysthYiIKIfBg4GnnpLRR8OHq65GVwwvlkhKkqHSBgMQEKC6GiIiomwuLsCsWYCbG/DDD8CaNaor0g3DiyXMrS7lywMeHkpLISIiyqVOHeCtt+R46FDg+nW19eiE4cUS5vBSqZLKKoiIiB7s/ffldSo+Hpg0SXU1umB4scTFi7KvUEFtHURERA9StGj2iKNPP80eJetEGF4scfmy7EuXVlsHERHRw3TqBHTrJjPCjxolA06cCMOLJRheiIjIUXz8sfTPjIwE1q9XXY1VMbxYguGFiIgcRdWqwIgRcjxqFJCRobQca2J4sYQ5vPj6qq2DiIgoP8aNA/z8gBMngBkzVFdjNQwvlrh6VfYlS6qtg4iIKD98fIAPP5TjiROB5GS19VgJw4slbt2SfdGiausgIiLKr1dfBerXB65dAyZPVl2NVTC8WMIcXooUUVsHERFRfrm6Ah99JMczZgDnz6utxwoYXizB8EJERI4oPBx4+mkgLQ344APV1Tw2hhdLMLwQEZEjMhiA//xHjufPB06dUlvPY2J4ya/MzOxhZgwvRETkaJ56CujYUV7Pxo9XXc1jYXjJr9u3s48ZXoiIyBGZRx4tW+bQywYwvOSXuzswezbwf//HFaWJiMgxNWwIvPiiLBfgwCOPDJrmXAsepKSkwGg0wmQywcfHx3r/8O3b2S0uJpOMnSciInI0Bw4AjRoBLi5AbCzw5JOqKwJg2es3W17yy9Mz+9jccZeIiMjRNGwofV+ysrKHUDsYhpf8MhgALy85ZnghIiJH9u67sv/qK4ec94XhxRLm20YML0RE5MiaNwdatpRRtB9/rLoaizG8WILhhYiInMW4cbKfOzd77T4HwfBiCYYXIiJyFm3aAHXqADdvysR1DoThxRLFisn+xg21dRARET0ugwEYPlyOv/gCuHNHbT0WYHixRKlSsneSJcWJiKiQ69NHXtvOnQPWrFFdTb4xvFjC11f2DC9EROQMihQBXn9djj//XG0tFmB4sUTp0rK/fFltHURERNbyxhuAqyuwbRtw6JDqavKF4cUSbHkhIiJnU6EC0L27HM+bp7aWfLJJeJk5cyYqV64MLy8vBAcHY/v27Q88d9WqVWjbti1Kly4NHx8fhIaG4pdffrFFmY/GlhciInJGgwbJ/ptvHGJEre7hZfny5RgxYgTGjRuHAwcOoEWLFujQoQPi4uLyPH/btm1o27Yt1q9fj3379qFVq1Z47rnncODAAb1LfbTy5WUfH6+2DiIiImtq0wYIDASuXQNWrVJdzSPpvjBjSEgIGjVqhFmzZt19LCgoCN26dcPkfK5oWbt2bfTs2RPvv//+I8/VbWFGANi/HwgOBvz9gcRE6/7bREREKk2aBIwfD7RqBWzaZPMvbzcLM6anp2Pfvn0IDw/P8Xh4eDiioqLy9W9kZWUhNTUVJUuWzPPzaWlpSElJybHpJjBQ9pcuOUSzGhERUb4NGCBzv2zeDJw6pbqah9I1vCQnJyMzMxP+/v45Hvf390diPlsuPvnkE9y4cQM9evTI8/OTJ0+G0Wi8uwUEBDx23Q9UsiRQvLgcP+C2FxERkUMKCADatZPjxYvV1vIINumwazAYcnysaVqux/KybNkyTJgwAcuXL4efn1+e54wdOxYmk+nuFq9nfxSDIbv15dw5/b4OERGRCn37yn7ZMkDfXiWPRdfw4uvrC1dX11ytLElJSblaY+63fPlyDBw4ECtWrECbNm0eeJ6npyd8fHxybLqqUkX2J0/q+3WIiIhsrWtXmbju5Enp52mndA0vHh4eCA4ORmRkZI7HIyMjERYW9sDnLVu2DK+88gqWLl2KTp066Vmi5WrVkv2xY2rrICIisrbixYHOneX422/V1vIQut82GjVqFObNm4cFCxYgJiYGI0eORFxcHIYMGQJAbvv069fv7vnLli1Dv3798Mknn6BZs2ZITExEYmIiTCaT3qXmT+3asmd4ISIiZ/Tyy7L/9lsgK0ttLQ+ge3jp2bMnpk6dikmTJqFBgwbYtm0b1q9fj8C/+44kJCTkmPPlyy+/xJ07d/Dmm2+ibNmyd7fh5pUvVTO3vBw9qrYOIiIiPXToAPj4AOfPA/kcGWxrus/zYmu6zvMCADduZI84SkrKnnWXiIjIWfTtCyxZAoweDUyZYpMvaTfzvDilYsWAypXl+I8/1NZCRESkhy5dZL9mjdo6HoDhpSAaNZL93r1q6yAiItJDu3aAuzsQGwucOKG6mlwYXgqiSRPZ//672jqIiIj0YDQCzzwjx2vXKi0lLwwvBdG0qewZXoiIyFk995zsGV6cRHCwzLZ77hxw+bLqaoiIiKzPHF527AD0XDewABheCsLHB6hRQ4737FFbCxERkR4qVQKqVgUyM4Ft21RXkwPDS0GFhsp++3a1dRAREemldWvZb9qkto77MLwUlLkj05YtKqsgIiLSjzm8/Pab2jruw/BSUC1byn7vXiA1VW0tREREemjVSvaHD8vErHaC4aWgAgNlsrrMTGDnTtXVEBERWV/p0kC9enJsR3caGF4ehzmR2tF/KBERkVW1aCH7XbvU1nEPhpfHYe73EhmptAwiIiLdNGsme4YXJ9Guncz3sn8/kJCguhoiIiLrM4eX/fuBtDS1tfyN4eVx+PllLxWwfr3aWoiIiPRQtSpQqpQEl0OHVFcDgOHl8XXqJPt169TWQUREpAeDwe5uHTG8PK6OHWUfGWk3zWlERERWZb7LcOCA2jr+xvDyuBo1Avz9gevXga1bVVdDRERkfXXryv6PP9TW8TeGl8fl4gJ07SrHK1eqrYWIiEgP5vBy9KjMb6YYw4s19Ogh+1WrgIwMtbUQERFZW5UqQJEiwO3bwOnTqqtheLGKli1lFsIrV4DNm1VXQ0REZF2urkDt2nJsB7eOGF6swc0NeOEFOV6xQm0tREREeqhTR/ZHj6qtAwwv1nPvraP0dLW1EBERWVu1arI/c0ZtHWB4sZ6nnwbKlAGuXgV+/ll1NURERNZVpYrsGV6ciKsr0LevHC9cqLYWIiIiazOHF3bYdTIDBsh+3TogKUltLURERNZUtarsL14Ebt1SWgrDizXVqgU0bQrcuQN8843qaoiIiKynZEnAx0eO//xTaSkML9Zmbn1ZuBDQNLW1EBERWYvBAJQrJ8eJiUpLYXixtl69AC8v4MgRYPdu1dUQERFZT5kysmd4cTIlSgA9e8rxjBlKSyEiIrIqf3/ZX7qktAyGFz0MHSr7FSuU/wcTERFZDVtenFjjxkBIiExWN2+e6mqIiIisw9zywvDipMytL7NmyegjIiIiR+frK/u//lJaBsOLXl56SRZrvHAB+OEH1dUQERE9PvNQ6dRUpWUwvOjF0xMYMkSO/+//OGyaiIgcn7e37FNSlJbB8KKnoUNl2PSePcDWraqrISIiejyFqeVl5syZqFy5Mry8vBAcHIzt27c/9PytW7ciODgYXl5eqFKlCmbPnm2LMq3Pzw949VU5/ugjtbUQERE9ruLFZe/s4WX58uUYMWIExo0bhwMHDqBFixbo0KED4uLi8jz/7Nmz6NixI1q0aIEDBw7g3XffxbBhw/D999/rXao+3noLcHEBNmwADh5UXQ0REVHBeXrKPj1daRkGTdO3M0ZISAgaNWqEWbNm3X0sKCgI3bp1w+TJk3Od/84772DNmjWIiYm5+9iQIUNw6NAhREdHP/LrpaSkwGg0wmQywcfcvKVa797AsmXAyy8DS5eqroaIiKhgTp0CqlWTvi9W7vdiyeu3ri0v6enp2LdvH8LDw3M8Hh4ejqioqDyfEx0dnev8du3aYe/evcjIyMh1flpaGlJSUnJsduedd2S/fDkQG6u2FiIiooJyd5e94pYXXcNLcnIyMjMz4W+e1OZv/v7+SHzABDeJiYl5nn/nzh0kJyfnOn/y5MkwGo13t4CAAOt9A9ZSvz7w3HNAVhYwcaLqaoiIiAqmSBFZAqdXL6Vl2KTDrsFgyPGxpmm5HnvU+Xk9DgBjx46FyWS6u8XHx1uhYh1MmiT7b7+VRRuJiIgcTXq63EVQ3AVC1/Di6+sLV1fXXK0sSUlJuVpXzMqUKZPn+W5ubihVqlSu8z09PeHj45Njs0sNGgAvvCDzvUyYoLoaIiIiy5lvF3l4KC1D1/Di4eGB4OBgREZG5ng8MjISYWFheT4nNDQ01/kbN25E48aN4W6+1+aoJk4EDAbg++858oiIiByPue+pM4cXABg1ahTmzZuHBQsWICYmBiNHjkRcXByG/D377NixY9GvX7+75w8ZMgTnzp3DqFGjEBMTgwULFmD+/Pl4++239S5Vf7VrZ98nfO89tbUQERFZytzyorgxwU3vL9CzZ09cuXIFkyZNQkJCAurUqYP169cjMDAQAJCQkJBjzpfKlStj/fr1GDlyJGbMmIFy5cph2rRpeOGFF/Qu1TYmTABWrAB++gnYsgV45hnFBREREeXT9euyL1ZMaRm6z/Nia3Y5z8v93nwTmDkTaNQI+P13mcSOiIjI3m3cCLRrJ6Nordz9wW7meaEHmDBB1ofYvx9YskR1NURERPljnktNceMAw4sKpUsD774rx+++C9y8qbYeIiKi/DCHF/Pq0oowvKgyfDhQsSJw/jzw2WeqqyEiInq0v/6SfYkSSstgeFHFywswr+00ebKEGCIiInt26ZLsy5RRWgbDi0q9egHNmwM3bgAjR6quhoiI6OHMk8gyvBRiLi4y6sjVFVi5UnpxExER2SuGFwIA1KsHDB0qx0OHAmlpaushIiJ6EPNtowcs8WMrDC/2YOJESbEnTwIff6y6GiIiotw0DTBPKlu+vNJSGF7sgdEIfPKJHH/4IXD6tNp6iIiI7vfXX4DJJMeVKystheHFXrz8MtCqFXD7NjBokCRcIiIie2F+Y122LFC0qNJSGF7shcEAzJ0LFCkCbN4sx0RERPbCHF6qVlVbBxhe7EvVqsB//iPHb7/NuV+IiMh+MLzQAw0bBjRrBqSmAkOG8PYRERHZh5gY2deoobYOMLzYH1dXYP58wMMDWLeOCzcSEZF9OHRI9vXrq60DDC/2qVYt4L335Pif/wTi49XWQ0REhVtaGnD8uBzXq6e2FjC82K933gGaNgWuXQP69weyslRXREREhVVMDJCZCTzxhPI5XgCGF/vl7g58840MR9u8mStPExGROuZbRvXqyehYxRhe7Fm1atmh5d13gcOH1dZDRESF0549sm/YUG0df2N4sXeDBgHPPQekpwN9+sgkdkRERLYUFSX75s3V1vE3hhd7ZzAA8+YBfn7AkSPA6NGqKyIiosIkNTW75T8sTG0tf2N4cQR+fsCiRXL8xRfAd98pLYeIiAqRPXtk0EhgIFCunOpqADC8OI4OHWQEEgAMHAicOqW2HiIiKhzMt4zspNUFYHhxLB9+KPcbU1OBHj3Y/4WIiPT366+yf/pptXXcg+HFkbi5Ad9+C/j6AgcOAKNGqa6IiIicWWoqEB0tx+Hhamu5B8OLo6lQAVi8WI5nzQKWLVNbDxEROa+tW4GMDFmMsUoV1dXcxfDiiNq3l3lfAOn/cvCg0nKIiMhJbdwoeztqdQEYXhzXpElAu3bArVtAt25AcrLqioiIyJloGrBhgxwzvJBVuLrKLaOqVYFz56QD7507qqsiIiJncewYcPIk4OEBtG6tupocGF4c2RNPAD/+CBQrJusfcQI7IiKylu+/l314OODtrbaW+zC8OLratbM78E6dCnz1ldJyiIjISaxaJfsXXlBbRx4YXpxB9+7A++/L8euvA9u2qa2HiIgc2+nTspK0q6usr2dnGF6cxfjxko7T0yXMnDihuiIiInJU5mVoWrYESpVSW0seGF6chYuL3D4KCQH++gvo1IkjkIiIyHKalt0FoXdvtbU8AMOLMylSRDrwVqokax9168YlBIiIyDK//w4cPy6vKS+9pLqaPOkaXq5evYqIiAgYjUYYjUZERETg2rVrDzw/IyMD77zzDurWrYtixYqhXLly6NevHy5evKhnmc7F3x9Ytw4wGoGdO4FXX5UUTURElB/mVpfu3QEfH7W1PICu4aV37944ePAgNmzYgA0bNuDgwYOIiIh44Pk3b97E/v378d5772H//v1YtWoVTpw4gS5duuhZpvOpVUuGuLm5yVww5tWoiYiIHiYtTdbQA4D+/dXW8hAGTdPnbXlMTAxq1aqFXbt2ISQkBACwa9cuhIaG4vjx46hRo0a+/p3ff/8dTZs2xblz51CxYsVHnp+SkgKj0QiTyQQfO02MNvPVV8Arr8jx//0f8PbbSsshIiI7t3Qp0KcPUL68TIDq6mqzL23J67duLS/R0dEwGo13gwsANGvWDEajEVFRUfn+d0wmEwwGA0qUKJHn59PS0pCSkpJjo7/17w9MmSLHo0cDixYpLYeIiOzcF1/IfvBgmwYXS+kWXhITE+Hn55frcT8/PyQmJubr37h9+zbGjBmD3r17PzCFTZ48+W6fGqPRiICAgMeq2+mMHp3d4vLaa8DatWrrISIi+7RvHxAdDbi7A4MGqa7moSwOLxMmTIDBYHjotnfvXgCAwWDI9XxN0/J8/H4ZGRno1asXsrKyMHPmzAeeN3bsWJhMprtbfHy8pd+S85syRW4fZWbKGkicxI6IiO43Y4bsX3oJKFNGbS2P4GbpE4YOHYpevXo99JxKlSrh8OHDuHTpUq7PXb58Gf7+/g99fkZGBnr06IGzZ89i06ZND7335enpCU9Pz/wVX1gZDMDcucCVK9Ly8txzwKZNQHCw6sqIiMgeXL4sAzwAYOhQtbXkg8XhxdfXF76+vo88LzQ0FCaTCXv27EHTpk0BALt374bJZEJYWNgDn2cOLidPnsTmzZtRyg5n9nNIbm7A8uVA+/bS8tK2rSzmWL++6sqIiEi1adNkXrDGjYFmzVRX80i69XkJCgpC+/btMWjQIOzatQu7du3CoEGD0Llz5xwjjWrWrInVq1cDAO7cuYMXX3wRe/fuxZIlS5CZmYnExEQkJiYiPT1dr1ILjyJFgJ9+AkJDgatXgTZtgCNHVFdFREQqpaRkd9QdO1Za6+2crvO8LFmyBHXr1kV4eDjCw8NRr149LDavgPy32NhYmEwmAMD58+exZs0anD9/Hg0aNEDZsmXvbpaMUKKH8PYGfv5Z0nVysgSY48dVV0VERKrMng1cuwbUrCkzszsA3eZ5UYXzvOTTX38BrVsDBw8CZcsCW7cC1aqproqIiGzp9m1ZUubSJZlOQ+HEdHYxzwvZuZIlgchIoE4dICEBePZZ4ORJ1VUREZEtzZkjwaViRbtdhDEvDC+Fma8v8NtvQFAQcP68LH1+7JjqqoiIyBZSU4EPP5TjceNkfhcHwfBS2Pn5yaijunWlBeaZZ4BDh1RXRUREeps6VYZIV6sGDBiguhqLMLyQrES9ebPM+3L5MtCqFfD3RINEROSEkpNlzTsA+OADh2p1ARheyKxUKeDXX7OHUbduDezcqboqIiLSw+TJctuoYUOZUdfBMLxQthIlgF9+AZ5+Wsb9t2sngYaIiJzH8eMyKR0gIcbF8aKA41VM+jLPAxMeDty4AXTsKDPzEhGR49M0YORI4M4doHNneZPqgBheKLeiRYE1a2QRx4wM4OWXgenTVVdFRESPa906YMMGwMMD+Owz1dUUGMML5c3TE1i6FHjzTUnqw4YB//63HBMRkeNJSwNGjJDjUaOAJ59UWs7jYHihB3N1lRaXDz6Qj//zH+D116W5kYiIHMvkycDp00C5cjKviwNjeKGHMxikxWXOHOnUNW8e8MIL0h+GiIgcw5EjwH//K8effQYUL662nsfE8EL5M2gQsHKl3E5as0ZGJF28qLoqIiJ6lMxM4LXXpA9jly4OOTT6fgwvlH/duwObNsmyAvv3AyEhnI2XiMjeTZ8O7N4N+PgAM2dKi7qDY3ghy4SFyS9BzZqyHtJTTwHr16uuioiI8nLqVHb/lilTgPLl1dZjJQwvZLkqVYCoKFmJ+vp14LnngBkzVFdFRET3ysgA+vQBbt6UdesGDVJdkdUwvFDBPPGETGb36qtAVhYwdKhsGRmqKyMiIkBWjN6zBzAaga++csiZdB/Eeb4Tsj0PDxl9ZO7BPmMG0LatLO5IRETqREVJeAGA2bOBihXV1mNlDC/0eAwGYOxY4McfZWmBrVuBxo2lQy8REdmeyQT07Sut4n37Ar16qa7I6hheyDq6dJGOvNWqAXFxQPPmMkMvERHZjqYBr7wCnD0LBAYCX3yhuiJdMLyQ9QQFyf3Vjh2B27elo9jo0ZyRl4jIVj75BPjhB7mt/9130t/FCTG8kHWVKCGT2L37rnz88ceyQnViotKyiIic3tatwJgxcvz550CTJmrr0RHDC1mfq6usg7RiBVCsGLB5M9CwIbBli+rKiIicU0IC0LOnzKYbEQEMHqy6Il0xvJB+XnoJ2LsXqFNHWl5at5aRSVlZqisjInIet24BXbsCly7J39vZs51iFt2HYXghfdWsKR15+/eX0DJuHNC5M3DliurKiIgcX1aWdND9/XegVCnp71K0qOqqdMfwQvorWhRYuBCYPx/w8pLJ7Ro2lHkIiIio4CZOlFv07u7AqlVA1aqqK7IJhheyDYNBZuPdtUuGU8fHy8rUH3wg92iJiMgyy5YBkybJ8Zdfyt/UQoLhhWyrfn3pB9O7t4SW99+XNTfOnVNdGRGR4/jtN7ldBMiUFAMGKC3H1hheyPZ8fIAlS4DFi2VW3h07JNR8+63qyoiI7N++fUC3bkB6OvDii8DkyaorsjmGF1Knb1/g4EGgWTOZzvrll6Vjb2qq6sqIiOzTiRNAhw7A9evAs88C33wj01MUMgwvpFaVKsC2bcB778mKp19/DTRoII8REVG2ixdl0s/Ll4FGjWRkkaen6qqUYHgh9dzdpdPZli2y8umZM9IPZuRImb+AiKiwS0iQlpZz54Ann5RRm97eqqtShuGF7EeLFsDhw8DAgbK42NSp0gqza5fqyoiI1Ll0SYJLbKy8wYuMBPz8VFelFMML2RejEZg3D1i3DihXTu7vNm8u63Xcvq26OiIi20pKkuBy/DgQECDLrVSqpLoq5RheyD517AgcOSJrdGRlAR99BAQHyyySRESFgTm4HDsGlC8vwaVKFdVV2QWGF7JfTzwhHXh/+EGaSI8dk5FJI0ZwRBIRObe4OLmVfvSotEJv2VJoZs/ND13Dy9WrVxEREQGj0Qij0YiIiAhcu3Yt388fPHgwDAYDpk6dqluN5AC6dpVf4L59pRXm88+B2rXl1hIRkbOJjQWeekpum1esKMHlySdVV2VXdA0vvXv3xsGDB7FhwwZs2LABBw8eRERERL6e+8MPP2D37t0oV66cniWSo/D1lUntNmyQ+73x8bLAY8+esmI1EZEz2L9fWlzi42Vh2507ZUkVykG38BITE4MNGzZg3rx5CA0NRWhoKObOnYuffvoJsbGxD33uhQsXMHToUCxZsgTu7u56lUiOqF076QszerRMzLRiBRAUJJ18s7JUV0dEVHBbtwKtWsk8LsHBMt9VhQqqq7JLuoWX6OhoGI1GhISE3H2sWbNmMBqNiHrIasJZWVmIiIjA6NGjUbt27Ud+nbS0NKSkpOTYyMkVKwZMmSKddxs1Aq5dAwYNkmbW/ftVV0dEZLnFi4G2bYGUFFlgcdMmoHRp1VXZLd3CS2JiIvzyGIfu5+eHxIc083/00Udwc3PDsGHD8vV1Jk+efLdPjdFoREBAQIFrJgfTsCGwezfwyScSaKKjgcaNgTfeAP76S3V1RESPpmnAhAlAv35ARgbw0ktye9zHR3Vlds3i8DJhwgQYDIaHbnv37gUAGAyGXM/XNC3PxwFg3759+Pzzz7Fo0aIHnnO/sWPHwmQy3d3i4+Mt/ZbIkbm5AaNGSQe3l1+WPwSzZgHVqwNz5/JWEhHZr7Q0CS0TJ8rHY8bIArVFiqitywEYNE3TLHlCcnIykpOTH3pOpUqVsHTpUowaNSrX6KISJUrgs88+w4A8lu+eOnUqRo0aBReX7EyVmZkJFxcXBAQE4M8//3xkfSkpKTAajTCZTPBhci18tmwBhg6V0UkA0KQJMGOG7ImI7EViorSy7Ngh/fdmzZLb34WYJa/fFoeX/IqJiUGtWrWwe/duNG3aFACwe/duNGvWDMePH0eNGjVyPefKlStISEjI8Vi7du0QERGBAQMG5Pmc+zG8EDIygC++AMaPl/lgDAZ5d/Of/8hET0REKu3eDTz/vCy06OMDrFwp/V0KOUtev3Xr8xIUFIT27dtj0KBB2LVrF3bt2oVBgwahc+fOOUJIzZo1sXr1agBAqVKlUKdOnRybu7s7ypQpk6/gQgRAFnocOVLmSIiIkFtJX30lww3Hj5el5ImIVJg3TzrkXrwoIyX37GFwKQBd53lZsmQJ6tati/DwcISHh6NevXpYvHhxjnNiY2NhMpn0LIMKqzJlZIbe3btlfaRbt2T16urVgQULgMxM1RUSUWGRlgYMGSK3htLTge7d5W8T35gXiG63jVThbSPKk6YBq1YB//oXcOaMPFavnoxUatNGbW1E5NxOngR69ZKpHAwG4MMPpXOuC1fouZdd3DYisisGA/DCC7I+0scfy+rVhw9Lc227dsC+faorJCJn9M03Mh/V/v1AqVKyrMm77zK4PCZePSpcPD2Bt94CTp8Ghg2TodYbN8r8MC++CMTEqK6QiJzB9evAK69Iv7vr14GWLYFDh4AOHVRX5hQYXqhwKlVKFniMjZU/LgYD8P33QJ06wIABwLlzqiskIke1Z4+8IfrqK2lhmTgR+O03jna0IoYXKtyqVJFOvYcPA926yaR2ixbJyKRhw7joIxHlX3o68O9/A2Fh8saoXDmZ5v/992UuF7IahhciQFpcVq8Gdu0Cnn1W5oqZPh2oXBkYMUKGNRIRPcihQ0DTpjKfVGamzPh9+LDcLiKrY3ghuldIiDTv/vqrHN++LbeXqlSRmXu5/AQR3SsjQwJLkyYSYHx9ge++A5YuldvTpAuGF6K8tG4tCz1u3ChzxKSlyTIDVavKXA35WKqCiJxcdLSMJPr3vyXEdOsGHDkinf9JVwwvRA9iMMhQ6u3b5b71M8/IH6gvv5Q+MQMHyn1tIipcrl0D/vEPeWNz5Ii0sCxeLHNJ+furrq5QYHghehSDAWjVCti8Gdi6VSa1u3NHZukNCpJ3Wzt3qq6SiPSmacCKFfJ7P3u2fDxgAHD8ONC3r/ytIJtgeCGyxNNPA5GRQFQU0LWr/PH68UfgqafkXdgPP8iIJSJyLocPy+3knj1lFGL16vKGZsEC6edCNsXwQlQQoaESVGJigNdeAzw8JNB07w7UqgXMnSudfYnIsSUnA2+8ATRsKGHFy0sWeD18WG4lkxIML0SPo2ZNCSp//gmMHQuUKCH9YF5/HahYUTryXbigukoislRGBjBtmvRvmzVLWlRfeknesEyYILN1kzIML0TWULYs8N//AnFxwKefAgEBwOXLMoQyMFCamnfskNtMRGS/srKA5culBXX4cOmc26CB9HdbsQKoVElxgQQwvBBZl7c3MHKkrFy9cqVMUJWZKX/0WrSQYZULFwK3bqmulIjupWnAL7/ItP69egGnTgF+fsCcOcDevdLfjewGwwuRHtzcZBXrLVuAgwelX4yXlxy/+qq0zIweDZw4obhQIro7s3b79sCBA/Im5IMPZAHXQYM4tb8dMmiac7Vjp6SkwGg0wmQywcfHR3U5RNmuXAHmz5fJ7uLish9v2VL6yDz/vAQcIrKNHTskpGzcKB97espM2mPGcASRApa8fjO8ENnanTvAunXS0ffnn7OHVpcsCfTrJ+/0atVSWyORs9I0mXTygw+kHwsgLSv9+0tH3IAApeUVZgwvDC/kKOLjZZ6I+fNzrpsUFiaTX730EmA0qquPyFlkZcmbhf/8R6b1BwB3d/k9GzNGFmElpRheGF7I0WRmStP1nDnA2rXyMSC3kbp2BSIigPBw+WNLRPl365ZM3f/ZZzITLiC/V4MGSb8ztrTYDYYXhhdyZAkJwNdfy3bsWPbjfn5A795ya6lBA05FTvQwiYnAzJkyR0tysjzm7Q0MHgy89RZQpoza+igXhheGF3IGmiYjH77+Gli6VOaNMatdW4Zz9uwpk2gRkfzO/P67BJalS4H0dHk8MFDmbBk4EODrgt1ieGF4IWeTkSG3lb7+WtZSSkvL/lyDBhJievQAqlRRViKRMqmpElZmz5bpCMxCQ2Xepe7dZfoCsmsMLwwv5MyuXQNWrQK++w749VcZvWTWuLGEmJde4kyg5PwOHAC+/BJYsgS4fl0e8/SU34F//EPCCzkMhheGFyosrlwBVq+WGXw3bcru6AvIQnJdu8pWvz77yJBzSEiQVpavv5bFEc2qVweGDJE+YaVKqauPCozhheGFCqPLl6VFZsUKmdnXPH8MIItEdukiQaZlS45aIsdy86bcLv36a7l9av7Z9vAAunWTVpaWLRnQHRzDC8MLFXaXL8tEeD/+KH/sb97M/pzRCHTsKFt4uIxiIrI3aWnys7typbQupqZmfy40VFpYevSQyR3JKTC8MLwQZbt1S/rG/PijzCGTlJTz88HBQLt2sq5Ls2ZslSF1bt+WxRFXrgTWrAFSUrI/V6mSzHcUEcERdk6K4YXhhShvWVnA7t0SYjZskA6P9/LxAVq3liDTpo3MOsqmeNLT1avys7h2LfDTTzlbWMqVA158UbbmzQEXriXszBheGF6I8ufSJWma37BB9ubJvMwqVgSeeQZo1Ur2HMFEj0vTgJgYCSrr1gE7d+bsaF6hgoSVl16SlkAGlkKD4YXhhchyWVnA/v0SZH75RVpoMjJynlOpUnaYefppmfyLLTP0KFevAps3A7/9JusLnT2b8/O1awOdO0uH8pAQBpZCiuGF4YXo8d24IQvYbd4so5f27Mk5pwwAlC0rnSdDQ2UxyUaNZN0YKtxu3gR27JCw8ttvEorvfanx9JQA3Lkz0KkTW/QIAMMLwwuRHq5fB6KiJMxs3gzs25c7zHh4yPwyYWESaIKD2W+mMEhOlqC7c6dse/ZkT81vFhQEPPss0Lat9KcqVkxNrWS3GF4YXoj0d+uWBJioKHnhiorKPZIJAEqUkBYZ8xYcDDz5JG8NOKrMTCA2Nvv/fOdO+fh+AQHS+bt1awkt5crZvlZyKAwvDC9Etqdp0pfBHGZ27wb++CP3O3AAKF5cWmjq1QPq1JE+D7Vrc84Oe5ORIZ1r9+2TWz/798vaQffOG2RWs6aMCGreHHjqKQmobHEjC9hNeLl69SqGDRuGNWvWAAC6dOmC6dOno0SJEg99XkxMDN555x1s3boVWVlZqF27NlasWIGKFSs+8msyvBDZkfR04OjR7Be+ffuAQ4dkPo+8lC2bHWbq1JFbDU8+CZQuzRdCPWVlAXFxwLFj8v917Bhw5IiEz3sXATUrVkxa0Jo3z75FyCn56THZTXjp0KEDzp8/jzlz5gAAXn/9dVSqVAlr16594HNOnz6Npk2bYuDAgXj55ZdhNBoRExODJk2awC8fM4EyvBDZuTt3gOPHJcwcOSLb0aPy4vkgPj4SYqpVy7l/8kmZIZjBJn/++gs4fVq2M2eAEyfk2sfESAftvPj45L7tV60a4Opq29rJ6dlFeImJiUGtWrWwa9cuhISEAAB27dqF0NBQHD9+HDVq1Mjzeb169YK7uzsWL15coK/L8ELkoFJSst/5mwNNbCwQH59zpMr9PD1lbpCKFaWfxf37MmXkdpSzB5zMTCAxEbhwIXs7f15CijmsXLv24Oe7uwM1agC1aknLV61aQIMGQJUq7J9ENmHJ67ebXkVER0fDaDTeDS4A0KxZMxiNRkRFReUZXrKysrBu3Tr861//Qrt27XDgwAFUrlwZY8eORbdu3fQqlYjsgY+PTErWrFnOx2/flhfeU6eAkyez9ydPSrBJS8tuTXgQNzdpofH3z96bj0uVkk7F5s1ozN6ral3QNOlXkpoKmEyyenhysmz3HyckSFBJTMw52duDlCkDVK0q25NPZoeVqlW5NAQ5DN3CS2JiYp63efz8/JCYmJjnc5KSknD9+nX873//w4cffoiPPvoIGzZswPPPP4/NmzejZcuWuZ6TlpaGtHvuyabcuxYGETk+Ly95ga1VK/fn0tOBixflllN8vOzvPY6Pl9aGO3fkvIsXLfva3t7SubhIkbw3Ly9p+XF1lZYdFxfZ7j0GpONrerrs7z1OT5fwdf26bKmp2ccFaRR3dZVwUqECUL68bJUqZYeVypU5RJmcgsXhZcKECZg4ceJDz/n9998BAIY8mmk1TcvzcUBaXgCga9euGDlyJACgQYMGiIqKwuzZs/MML5MnT35kPUTkpDw85MX5YZOcpaXJEO6kJFkO4dKlnMdXr0rAuXZNWjmuXcseTZOamnOtHVszGCRA+fpmb6VK5Tz2988OKv7+7ItChYLF4WXo0KHo1avXQ8+pVKkSDh8+jEuXLuX63OXLl+Hv75/n83x9feHm5oZa973DCgoKwo4dO/J8ztixYzFq1Ki7H6ekpCAgIOBR3wYRFRaentL3xZK/C+npEmRMJgkvt27Jdvt29rF5S0uTVpKsrOz9vceaJrdjPDxkf++xh4ds5hae4sWzj729pXWH/U2IcrE4vPj6+sLX1/eR54WGhsJkMmHPnj1o2rQpAGD37t0wmUwICwvL8zkeHh5o0qQJYu+b8OjEiRMIDAzM8zmenp7w9PS08LsgInoIDw8Znl26tOpKiCgPukX6oKAgtG/fHoMGDcKuXbuwa9cuDBo0CJ07d87RWbdmzZpYvXr13Y9Hjx6N5cuXY+7cuTh16hS++OILrF27Fm+88YZepRIREZED0bU9csmSJahbty7Cw8MRHh6OevXq5RoCHRsbC5PJdPfj7t27Y/bs2ZgyZQrq1q2LefPm4fvvv8dTTz2lZ6lERETkILg8ABERESlnyes3e4IRERGRQ2F4ISIiIofC8EJEREQOheGFiIiIHArDCxERETkUhhciIiJyKAwvRERE5FAYXoiIiMihMLwQERGRQ2F4ISIiIodi8arS9s682kFKSoriSoiIiCi/zK/b+Vm1yOnCS2pqKgAgICBAcSVERERkqdTUVBiNxoee43QLM2ZlZeHixYvw9vaGwWBQXQ4ASZMBAQGIj48v1ItF8joIXgfB6yB4HQSvgyjM10HTNKSmpqJcuXJwcXl4rxana3lxcXFBhQoVVJeRJx8fn0L3w5gXXgfB6yB4HQSvg+B1EIX1OjyqxcWMHXaJiIjIoTC8EBERkUNheLEBT09PjB8/Hp6enqpLUYrXQfA6CF4HwesgeB0Er0P+OF2HXSIiInJubHkhIiIih8LwQkRERA6F4YWIiIgcCsMLERERORSGF51cvXoVERERMBqNMBqNiIiIwLVr1x75vJiYGHTp0gVGoxHe3t5o1qwZ4uLi9C9YJwW9DmaDBw+GwWDA1KlTdavRFiy9DhkZGXjnnXdQt25dFCtWDOXKlUO/fv1w8eJF2xVtBTNnzkTlypXh5eWF4OBgbN++/aHnb926FcHBwfDy8kKVKlUwe/ZsG1WqL0uuw6pVq9C2bVuULl0aPj4+CA0NxS+//GLDavVj6c+D2c6dO+Hm5oYGDRroW6CNWHod0tLSMG7cOAQGBsLT0xNVq1bFggULbFStndJIF+3bt9fq1KmjRUVFaVFRUVqdOnW0zp07P/Q5p06d0kqWLKmNHj1a279/v3b69Gntp59+0i5dumSjqq2vINfBbPXq1Vr9+vW1cuXKaZ999pm+herM0utw7do1rU2bNtry5cu148ePa9HR0VpISIgWHBxsw6ofz7fffqu5u7trc+fO1Y4dO6YNHz5cK1asmHbu3Lk8zz9z5oxWtGhRbfjw4dqxY8e0uXPnau7u7trKlSttXLl1WXodhg8frn300Ufanj17tBMnTmhjx47V3N3dtf3799u4cuuy9DqYXbt2TatSpYoWHh6u1a9f3zbF6qgg16FLly5aSEiIFhkZqZ09e1bbvXu3tnPnThtWbX8YXnRw7NgxDYC2a9euu49FR0drALTjx48/8Hk9e/bU+vbta4sSbaKg10HTNO38+fNa+fLltSNHjmiBgYEOHV4e5zrca8+ePRqAR/6xtxdNmzbVhgwZkuOxmjVramPGjMnz/H/9619azZo1czw2ePBgrVmzZrrVaAuWXoe81KpVS5s4caK1S7Opgl6Hnj17av/+97+18ePHO0V4sfQ6/Pzzz5rRaNSuXLlii/IcBm8b6SA6OhpGoxEhISF3H2vWrBmMRiOioqLyfE5WVhbWrVuH6tWro127dvDz80NISAh++OEHG1VtfQW5DoBci4iICIwePRq1a9e2Ram6Kuh1uJ/JZILBYECJEiV0qNK60tPTsW/fPoSHh+d4PDw8/IHfc3R0dK7z27Vrh7179yIjI0O3WvVUkOtwv6ysLKSmpqJkyZJ6lGgTBb0OCxcuxOnTpzF+/Hi9S7SJglyHNWvWoHHjxpgyZQrKly+P6tWr4+2338atW7dsUbLdYnjRQWJiIvz8/HI97ufnh8TExDyfk5SUhOvXr+N///sf2rdvj40bN6J79+54/vnnsXXrVr1L1kVBrgMAfPTRR3Bzc8OwYcP0LM9mCnod7nX79m2MGTMGvXv3dojF2pKTk5GZmQl/f/8cj/v7+z/we05MTMzz/Dt37iA5OVm3WvVUkOtwv08++QQ3btxAjx499CjRJgpyHU6ePIkxY8ZgyZIlcHNzjjWEC3Idzpw5gx07duDIkSNYvXo1pk6dipUrV+LNN9+0Rcl2i+HFAhMmTIDBYHjotnfvXgCAwWDI9XxN0/J8HJB3VwDQtWtXjBw5Eg0aNMCYMWPQuXNnu+u0qOd12LdvHz7//HMsWrTogefYCz2vw70yMjLQq1cvZGVlYebMmVb/PvR0//f3qO85r/PzetzRWHodzJYtW4YJEyZg+fLleQZgR5Pf65CZmYnevXtj4sSJqF69uq3KsxlLfh6ysrJgMBiwZMkSNG3aFB07dsSnn36KRYsWFerWF+eIszYydOhQ9OrV66HnVKpUCYcPH8alS5dyfe7y5cu5EreZr68v3NzcUKtWrRyPBwUFYceOHQUvWgd6Xoft27cjKSkJFStWvPtYZmYm3nrrLUydOhV//vnnY9VuTXpeB7OMjAz06NEDZ8+exaZNmxyi1QWQn2dXV9dc7yaTkpIe+D2XKVMmz/Pd3NxQqlQp3WrVU0Gug9ny5csxcOBAfPfdd2jTpo2eZerO0uuQmpqKvXv34sCBAxg6dCgAeRHXNA1ubm7YuHEjnn32WZvUbk0F+XkoW7YsypcvD6PRePexoKAgaJqG8+fPo1q1arrWbK8YXizg6+sLX1/fR54XGhoKk8mEPXv2oGnTpgCA3bt3w2QyISwsLM/neHh4oEmTJoiNjc3x+IkTJxAYGPj4xVuRntchIiIi1x/qdu3aISIiAgMGDHj84q1Iz+sAZAeXkydPYvPmzQ71Au7h4YHg4GBERkaie/fudx+PjIxE165d83xOaGgo1q5dm+OxjRs3onHjxnB3d9e1Xr0U5DoA0uLy6quvYtmyZejUqZMtStWVpdfBx8cHf/zxR47HZs6ciU2bNmHlypWoXLmy7jXroSA/D82bN8d3332H69evo3jx4gDkdcHFxQUVKlSwSd12SVVPYWfXvn17rV69elp0dLQWHR2t1a1bN9fQ2Bo1amirVq26+/GqVas0d3d3bc6cOdrJkye16dOna66urtr27dttXb7VFOQ63M/RRxtpmuXXISMjQ+vSpYtWoUIF7eDBg1pCQsLdLS0tTcW3YDHzkND58+drx44d00aMGKEVK1ZM+/PPPzVN07QxY8ZoERERd883D5UeOXKkduzYMW3+/PlONVQ6v9dh6dKlmpubmzZjxowc/+/Xrl1T9S1YhaXX4X7OMtrI0uuQmpqqVahQQXvxxRe1o0ePalu3btWqVaumvfbaa6q+BbvA8KKTK1euaH369NG8vb01b29vrU+fPtrVq1dznANAW7hwYY7H5s+frz355JOal5eXVr9+fe2HH36wXdE6KOh1uJczhBdLr8PZs2c1AHlumzdvtnn9BTVjxgwtMDBQ8/Dw0Bo1aqRt3br17uf69++vtWzZMsf5W7Zs0Ro2bKh5eHholSpV0mbNmmXjivVhyXVo2bJlnv/v/fv3t33hVmbpz8O9nCW8aJrl1yEmJkZr06aNVqRIEa1ChQraqFGjtJs3b9q4avti0LS/e8QREREROQCONiIiIiKHwvBCREREDoXhhYiIiBwKwwsRERE5FIYXIiIicigML0RERORQGF6IiIjIoTC8EBERkUNheCEiIiKHwvBCREREDoXhhYiIiBwKwwsRERE5lP8HbCC4QP2D3NAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9 = plt.figure()\n",
    "X1_range = [-0.7 + x / 10000 for x in range(0,20000)]\n",
    "X2_new_boundary1,X2_new_boundary2 = ff(X1_range)\n",
    "plt.plot(X1_range,X2_new_boundary1,c='r')\n",
    "plt.plot(X1_range,X2_new_boundary2,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "45866ea6-0ec4-4430-a799-cd77ecff1f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask], data.loc[:,'test2'][mask],c='green')\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask], data.loc[:,'test2'][~mask],c='pink')\n",
    "plt.title('test1 - test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "\n",
    "X1_range = [-0.7 + x / 10000 for x in range(0,20000)]\n",
    "X2_new_boundary1,X2_new_boundary2 = ff(X1_range)\n",
    "plt.plot(X1_range,X2_new_boundary1,c='r')\n",
    "plt.plot(X1_range,X2_new_boundary2,c='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55c2010-1cd6-4f1a-a320-7bb619ad84b9",
   "metadata": {},
   "source": [
    "* 边界函数\n",
    "$$\n",
    "w_1 x_1 + w_2 x_2 + b = 0\n",
    "$$\n",
    "\n",
    "* 二级边界函数\n",
    "$$\n",
    "\\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\theta_3 x_1^2 + \\theta_4 x_2^2 + \\theta_5 x_1 x_2 = 0\n",
    "$$\n",
    "\n",
    "* 二阶边界函数的标准形式\n",
    "$$\n",
    "\\theta_3 x_1^2 + (\\theta_1 + \\theta_5 x_2) x_1 + (\\theta_0 + \\theta_2 x_2 + \\theta_4 x_2^2) = 0\n",
    "$$\n",
    "\n",
    "* 求解x\n",
    "$$\n",
    "x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad4aa4c-5547-4dfb-bc93-43080d9a0ef0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1856a05a-442f-4731-a815-c0930f52b372",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b9ceb3-f172-43df-9e86-8b7ec6c98394",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26a0c285-a5c4-4426-80b8-0ee3e5d9d391",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e79c9a32-5034-4c48-af14-0ef5abb97cdf",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f60036-2a6e-4be1-9172-d33293ae1d45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18df5483-13b8-4d3a-8928-bfbdf7aa3b2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232054dd-820c-4381-b327-9aa77e693f78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce6baf49-c4ab-4f1f-850e-53486d548008",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cb2c877-9f19-45a5-bdaa-881b0c3e21cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a948354-2821-44d6-84df-2509db58a2fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dad21093-e715-4b54-af25-9bcaaa93e430",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe60bff-afdc-4db8-aab2-e0fcca38324a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "847f6d6a-1042-4b65-8fff-12b1a20c7e30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ff76f8-7550-4690-89dd-99a03a945648",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "048f6771-a2b7-46f7-9767-4a448d79b46e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d48017-1f11-422f-93be-8a93f01a6a42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b562415-a9ba-4b42-97e5-9671b3ce4cef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de88657e-3a43-4ac3-8d97-1cb18470a079",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035d3508-06a2-45b1-bb11-d72bf17338cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
